With the development of video compression, transmission, and storage, perceptual video quality is of great significance. For instance, determining the quality loss resulting from compression and transportation can be of interest for video distribution quality surveillance or video distribution services with video quality dependent charges.
Most precise and direct way for assessing video quality is subjective quality score assignment. But, subjective assignment is expensive and time-consuming. Thus, objective video quality measurement (VQM) has been proposed as an alternative method, in which it is expected to provide a calculated score as close as possible to the average subjective score assigned by subjects. According to the reference information about the source encoded and transmitted available or not at the decoder side, the objective video quality measurement can be categorized into three types: 1) Full-Reference (FR); 2) Reduced-Reference (RR); 3) No-Reference (NR). Since no reference is required in the NR video quality measurement, the NR methods are in particular useful for, but not limited to, evaluating perceived video quality of a video distorted by transmission.
In NR methods, mapping between objectively detectable artefact features and the prediction of subjective scores is crucial. There is a bouquet of methods in the art for establishing such mapping. For instance some mappings use a fixed formula with trained parameters, most of which are linear or exponential. Or, Artificial Neural Networks are trained to predict mean observer scores (MOS) from objectively detectable artefact features. Although artificial neural networks achieve good results for test data in problems where training and test data are related to similar content, it is not easy to achieve stable performance when extending to wide range of contents.
Further, there are semi-supervised learning methods in which a small quantity of labelled and a large number of unlabeled data can be involved into training together to achieve better performance.
In the prior art semi-supervised learning regression methods, the labelled and unlabelled data are collected and used to train the regressor. Then in the test process, the regressor will not be updated, and the test data will be evaluated.
In order to further improve semi-supervised learning regression methods, Zhi-Hua Zhou and Ming Li proposed in: “Semi-Supervised Regression with Co-Training”, IJCAI 2005: 908-916, an algorithm using two k-nearest neighbour regressors with different distance metrics, each of which labelling the unlabelled data for the other regressor during the learning process. The final prediction is made by averaging the regression estimates of both regressors.